The present invention relates to a self-extending shape neural-network which is capable of a self-extending operation in accordance with the study results.
Generally, there is a multilayer Perceptron type neural-network for studying in accordance with an error back propagation system. This multilayer Perceptron type neural-network is composed of an input layer, an intermediate layer and an output layer each having units, with the units of each layer being combined by synopsis coupling. When an input data is inputted into the unit of the input layer, the output data corresponding to the construction of the network is outputted from each unit of the output layer. The number of the units of the input layer is determined by the degree of the input data, and the number of the units of the output layer is set by the number of the categories which are to be discriminated. The number of the layers in the intermediate layer and the number of the units to be contained in each intermediate layer are different depending upon the usage and are determined through the trial and error considering recognition accuracy, processing time and so on. Unless the number of the intermediate layers and the number of the units to be contained in each intermediate layer are sufficient, the desirable output results (namely, category discrimination results) cannot be obtained in the studying by the back propagation. It is general to set the number of the intermediate layers and the number of the units to be included in each intermediate layer more than the necessary number to be expected in advance.
Also, there is a Kochnen type neural-network for studying by a self-systematic characteristic representation. This Kochnen type neural-network is composed of two layers, an input layer and an output layer, with a plurality of output nodes for representing the same category existing in the output layer. The category discriminating performance depends upon the number of the output nodes showing one category. It is general to set the number of the output nodes showing one category more than the necessary number to be expected in advance.
The node (unit) is composed of an input portion for receiving the input from the other node (unit), a calculating portion for calculating the inputted data with the given algorithm, and an output portion for outputting the calculated results.
In order to set the construction of the neural-network by the studying, it is general to set the number of the intermediate layers of a multilayer Perceptron type neural-network, the number of the units of the intermediate layers or the number of the nodes of the output layer of the Kochnen type neural-network more than the expected necessary number.
When the number of the layers and the number of the nodes (units) to be contained in each layer are set more as in the neural-network, the couplings among the respective nodes (units) increase, the amount of calculation amount increases when the signal from the previous node (unit) is converted by the given algorithm and is outputted into the next node (unit), causing the studying operation or the discriminating operation to be delayed. When the number of the layers and the number of the units to be contained in each layer are set less than the necessary number, the studying is not effected sufficiently (namely, the studying is not focused) with respect to the studying date. Also, there is also a problem of causing a case where the studying is not focused with the value becoming a local optimal value of the weight function (energy function) among the nodes.